Recognition of dominant driving factors behind sap flow of Liquidambar formosana based on back-propagation neural network method

2021 ◽  
Vol 78 (4) ◽  
Author(s):  
Jie Tu ◽  
Qijing Liu ◽  
Jianping Wu
Keyword(s):  
Neural Network ◽  
Sap Flow ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Driving Factors ◽  
Neural Network Method ◽  
Network Method

scholarly journals Neural network method for control valve cost estimation on the EPC project bidding

2018 ◽  
Vol 49 ◽  
pp. 02004 ◽  
Author(s):  
Gilang Almaghribi Sarkara Putra ◽  
Rendra Agus Triyono
Keyword(s):  
Neural Network ◽  
Cost Estimation ◽  
Back Propagation ◽  
Control Valve ◽  
Back Propagation Neural Network ◽  
Neural Network Method ◽  
Price Quotation ◽  
Network Method ◽  
Alternative Choice ◽  
The Cost

Cost estimation on the bidding phase is a crucial stage that determines the success of the Engineering, Procurement and Construction (EPC) project. If the cost offered to the client is too high then it could not compete with the other bidder, but if the cost offered are too low it can reduce profit margins and result in losses for the EPC companies. This paper describe the use of Back Propagation Neural Network method to help determine cost estimation. This method is applied specifically to determine control valve cost estimation on the bidding phase so that the retrieved costs will be accurate. When there is no technical and price quotation from vendors as well as the narrowness of the bidding processing time, this method can be an alternative choice to determine the price based on previous vendor quotation. In the future, this method could be developed and applied for other instrumentation equipment such as transmitter, switch, analyzer, control system and others to achieve total cost estimation of instrumentation equipment in EPC bidding proposal.

scholarly journals Inverse Analysis of Crack in Fixed-Fixed Structure by Neural Network with the Aid of Modal Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Dhirendranath Thatoi ◽  
Prabir Kumar Jena
Keyword(s):  
Neural Network ◽  
Structural Damage ◽  
Back Propagation ◽  
Strain Energy Release ◽  
Back Propagation Neural Network ◽  
Mode Shapes ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Fixed Structure

In this research, dynamic response of a cracked shaft having transverse crack is analyzed using theoretical neural network and experimental analysis. Structural damage detection using frequency response functions (FRFs) as input data to the back-propagation neural network (BPNN) has been explored. For deriving the effect of crack depths and crack locations on FRF, theoretical expressions have been developed using strain energy release rate at the crack section of the shaft for the calculation of the local stiffnesses. Based on the flexibility, a new stiffness matrix is deduced that is subsequently used to calculate the natural frequencies and mode shapes of the cracked beam using the neural network method. The results of the numerical analysis and the neural network method are being validated with the result from the experimental method. The analysis results on a shaft show that the neural network can assess damage conditions with very good accuracy.

scholarly journals OPTIMASI CONJUGATE GRADIENT PADA BACKPROPAGATION NEURAL NETWORK UNTUK PREDIKSI HASIL TANGKAP IKAN

SAINTEKBU ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 29-39
Author(s):  
A. Aviv Mahmudi
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Accurate Method ◽  
Back Propagation Neural Network ◽  
Market Process ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Average Value ◽  
Network Method

The need for fish catch by a company or fisherman in Rembang Regency affects market process and also welfare. The catch made by the fishermen is not on target, due to the weather and type of fishing gear. An accurate method is needed in making predictions and a correlation between catch and weather so that fisherman can get maximum predictions results, so that price adjustment can be made. The research was conducted using an experimental method, to determine the accuracy of the effect of the Conjugate Gradient on the Back Propagation Neural Network in obtaining the best value. Based on the results of the Cycle training test with the Conjugate Gradient Backpropagation Neural Network method, the smallest average value is obtained at the 400th Epoch compared to the Epoch Gradient Descent With Momentum method at Epoch 800.Thus it is proven that using the Conjugate Gradient Backpropagation Neural Network method is better with an average value of- MSE average 0.2223 in three stages of testing Training Cycle, Learning Rate and Hidden Layer.

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